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Table of contents (10 chapters)
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Front Matter
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Global Optimization Algorithms as Decision Procedures. Theoretical Background and Core Univariate Case
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Front Matter
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Generalizations for Parallel Computing, Constrained and Multiple Criteria Problems
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Front Matter
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Global Optimization in Many Dimensions. Generalizations through Peano Curves
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Nizhni Novgorod State University, Nizhni Novgorod, Russia
Roman G. Strongin, Yaroslav D. Sergeyev
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Institute of Systems Analysis and Information Technology, University of Calabria, Rende, Italy
Yaroslav D. Sergeyev
Bibliographic Information
Book Title: Global Optimization with Non-Convex Constraints
Book Subtitle: Sequential and Parallel Algorithms
Authors: Roman G. Strongin, Yaroslav D. Sergeyev
Series Title: Nonconvex Optimization and Its Applications
DOI: https://doi.org/10.1007/978-1-4615-4677-1
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media Dordrecht 2000
Hardcover ISBN: 978-0-7923-6490-0Published: 31 October 2000
Softcover ISBN: 978-1-4613-7117-5Published: 10 November 2013
eBook ISBN: 978-1-4615-4677-1Published: 09 November 2013
Series ISSN: 1571-568X
Edition Number: 1
Number of Pages: XXVIII, 704
Number of Illustrations: 1 b/w illustrations
Topics: Optimization, Software Engineering/Programming and Operating Systems, Computational Mathematics and Numerical Analysis, Theory of Computation, Algorithms, Engineering, general